Advances in automation technology are transforming the industrial landscape. Whether talking about artificial intelligence (AI), digital twins, or robotics, automation brings physical and cyber systems together for greater performance, efficiency, sustainability, and safety.
I have noticed a worrisome trend, though, and I am sure you have, too. Increasingly, the media describes how AI is coming for manufacturing jobs. With each new innovation in society, we see corresponding headlines and public concern about how that innovation will negatively impact the workforce. This has been true of the printing press, the automobile, and the computer.
As some believe, automation does not have to eliminate jobs; rather, it repurposes existing jobs and creates new ones. Jobs of the future will not always look like jobs of the past.
When it comes to separating fact from fiction about automation and the workforce, I want you to remember one key point.
Automation depends on people to make the world a better place.
Automation is not just about robots, ChatGPT, and other AI tools. It’s not even solely about control systems, really. People keep this automated and interconnected world humming together in harmony.
From a people-centered view, industrial and manufacturing work has historically been risky, with little opportunity for advancement or growth. In the best-case scenario, it has been monotonous, and in the worst case, it has been actively dangerous. Moving toward automation does not rule people out; it just takes them out of harm’s way.
Looking at this from the manufacturer’s perspective, it is a mark of tremendous progress that industrial work is no longer as dirty, dull or dangerous as it once was. And all that human intelligence, creativity and capacity that used to be at risk can be valued in new ways that make work and life safer and more efficient.
AI in Manufacturing
Going forward, I believe we will see more packaging and processing manufacturers reliant upon AI to identify bottlenecks and save costs through process automation. AI may even make contributions to greater workplace safety and efficiency by mitigating repetitive and dangerous tasks or conducting preventive maintenance analysis. Machine learning also may result in the early identification of potential issues that might otherwise be missed.
I heard of a great recent example: relying upon machine learning to identify maintenance issues like corrosion through image analysis. Advanced analytic engines predicted where corrosion issues began to develop, reducing the need for manual and potentially biased analysis. Instead of walking around with clipboards and cameras, the engineers were able to let the AI do the data collection through cameras they had positioned throughout the facility. Those images were captured on a regular cadence and analyzed by the AI to flag existing or developing issues. This freed up the engineers to work on solving the causes of the corrosion issues and prioritizing a plan of action.
Digital Twins and Simulation
Building upon the previous example of preventive maintenance, a digital twin is the notion of building a digital version of a physical asset. Using the digital twin, a company can collect machine data and feed it into a model that can simulate future operations, determine failure limits, plan maintenance activities, and even calculate how much longer the machine will be operational... all without the physical machine being stopped.
In packaging and processing, where line shutdowns can eat deeply into productivity and profits, reliance on these types of technologies is especially key. Using a digital twin, engineers can train “off the line” so they are up to speed and able to make the most of any needed shutdown time without needing to acclimatize to systems.
Repetition and Creativity
Many of the most successful implementations of AI and machine learning technologies rely upon AI or the machine to do the repetitive work, and humans to apply creativity and problem solving. In my corrosion scenario above, it is true that the engineers’ jobs changed, but it was a change that allowed them to use their skills elsewhere and more efficiently. If that is what we can expect from the future of work as manufacturers increasingly rely upon automation technologies, I think that is a very positive future for us all.
Claire Fallon is CEO and executive director of the International Society of Automation (ISA), a non-profit professional association founded in 1945 to create a better world through automation.
